Real-time alerts for abnormal item pricing

ABSTRACT

An item price that is noted in a transaction for an item is identified. Any discount or item price override is identified for the transaction. A catalogue price for the item is obtained. Similar items associated with the item are determined based on mapped transaction contexts for the item and the similar items within a multidimensional space. Similar item prices are obtained and a median price for the item and similar items is calculated. A real-time price alert is sent to a resource that is associated with processing or handling the transaction when the item price, adjusted for any discount or item price override, deviates (above or below) from the median price by a threshold amount.

BACKGROUND

Pricing plays a critical role in every consumer's purchase decision.Pricing mistakes are common, specifically in the large catalogs that aretypical in the grocery and department store retail segments. Retailersare struggling to Identify those errors early enough, so they avoidsignificant financial losses.

A typical retailer's catalog may contain hundreds of thousands of itemsthat change their price on a daily basis. The pricing system is oftenconnected to several systems, which can cause confusion leading tomiss-pricing, which is extremely hard to identify. When a product ismiss-priced, the retailers main concerns are: 1) if a product's price isnot market compliant, the price of the product will increase thelikelihood that the customer will shop at a competitor store; 2) erosionof customer trust; 3) profit and revenue loss—when a price glitch is infavor of the customer, it could trigger significant financial losses tothe retailer within a matter of minutes; 4) harm that occurs to theretailer's value proposition—when a price glitch is in favor of theretailer, it may damage the retailer's reputation, e.g., “every day lowprices;” and 5) legal risks related to price agreements withmanufacturers.

Thus, it is critical for retailers to monitor prices of items and toidentify price anomalies as soon as possible to avoid significantfinancial losses and damage to the retailer's revenue, brand, andcustomer satisfaction.

Retailers use a number of approaches to detect and rectify productmiss-pricing. For example, retailers may perform manual price checkingwhich is usually performed by the store manager. However, this solutionis very time-consuming and since there are so many items in the catalogand several managing systems, there is a high probability that pricingmistakes will be missed. Another approach is automatic price checkingbased on the item's historical price (time series) and competitorprices. However, since the market is extremely dynamic and prices areoften affected by seasonality, market excess/shortage and trends,comparing the pricing to the same item is not as effective as an ongoingdaily or even hourly comparison to the prices of similar item groups.

As a result, product/item price anomalies remain a major concern withinthe retail industry for which there is no real-time, accurate, andcost-effective solution.

SUMMARY

In various embodiments, methods and a system for real-time alerts forabnormal item pricing are presented.

According to an aspect, a method for real-time alerts for abnormal itempricing is presented. For example, an item code and a transaction itemprice for an item being processed in a transaction are received. A groupof other item codes that are similar to the item code are obtained basedon transaction contexts. A catalogue item price for the item code andother catalogue item prices for the other item codes are acquired. Adetermination is made as to whether to send a real-time price alert forthe transaction based at least on the transaction item price, thecatalogue item price, and the other catalogue item prices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a diagram of a system for real-time alerts for abnormal itempricing, according to an example embodiment.

FIG. 1B is a diagram representing a process flow of the system of FIG.1A, according to an example embodiment.

FIG. 2 is a diagram of a method for real-time alerts for abnormal itempricing, according to an example embodiment.

FIG. 3 is a diagram of another method for real-time alerts for abnormalitem pricing, according to an example embodiment.

DETAILED DESCRIPTION

FIG. 1A is a diagram of a system 100 real-time alerts for abnormal itempricing, according to an example embodiment. It is to be noted that thecomponents are shown schematically in greatly simplified form, with onlythose components relevant to understanding of the embodiments beingillustrated.

Furthermore, the various components (that are identified in the FIGS. 1Aand 1B) are illustrated and the arrangement of the components ispresented for purposes of illustration only. It is to be noted thatother arrangements with more or less components are possible withoutdeparting from the teachings of real-time alerts for abnormal itempricing presented herein and below.

The system 100A provides a mechanism by which non-standard deviations inprices for items are detected in real time and provided to transactionsystems, management applications, and/or Enterprise Resource Planning(ERP) systems. What constitutes non-standard deviations can be customdefined by the retailer. Each item is mapped to multidimensional spacebased on that item's context defined in transaction histories fortransactions that comprised the item. Similar items are determined basedon distances between the mapped items' contexts within themultidimensional space. For any given item being purchased in atransaction, the catalogue price for the item and the actual price(after any transaction promotion/discount is applied or any transactionoverride for the item price during the transaction) are obtained. Next,N similar items to the item are derived from the multidimensional space,the prices known for those similar items are obtained, and a medianprice for the N similar items is calculated. The actual transactionprice (after coupons/discounts or price override) for the item is thencompared to the median price for the N similar items and a deviation inprice is determined. The deviation is then compared to aretailer-defined threshold percentage deviation (retailer can set thethreshold percentage), which when exceeded causes an alert to be raisedin real time to the transaction systems, management applications, and/orERP systems.

Because similar items are determined based on the items' transactioncontexts, high-end items that command much higher prices will not beidentified as being similar to non high-end items that command lowerprices. This ensures that the prices of two items having a same itemtype but associated with different pricing schemes are not identified asbeing similar resulting in erroneous price anomaly alerts during atransaction comprising either a high-end item or a non high-end item.For example, wines comprise large variances in prices based on brand; ahigh-end wine's price for a given transaction will result in comparisonof that high-end wine's transaction price against a median wine pricefor similar high-end wine prices.

System 100A comprises an enterprise data store 110, an onlinetransaction system 120, in-store transaction terminals 130, user devices140, an ERP system 150, a management application (app) 160, an itemspace mapper and similarity manager 170, one or more machine-learningalgorithms (MLA) 180, and a price alert manager 190. System 100Acomprises a variety of computing devices, each of which comprises atleast one processor and a non-transitory computer-readable storagemedium comprising executable instructions. The executable instructionswhen executed by the corresponding processor from the correspondingnon-transitory computer-readable storage medium causes that processor toperform operations discussed herein and below with respect to thecomponents 110-190.

An “item code” represents an item from a given retailer's productcatalogue. Each item code's affinity/similarity to the other item codesdefines that item's vector in multidimensional space. Theaffinity/similarity and item code vector determined by Word2Vecalgorithms based on analysis of the retailer's transaction histories andproduct catalogue. An “item vector” is a mathematical expression showingpoints within the multidimensional space representing the contexts of agiven item.

Enterprise data store 110 includes a variety of enterprise data, such astransaction histories for transactions performed with a retailer. Othertypes of data may be included in enterprise data store 100 as well, suchas incentives available to consumers, item prices for the transaction,customer data for known customers (loyalty data, profile data, etc.),the transaction details for transactions of customers (including itemcodes for items), item or product catalogue data, and other informationcaptured and retained by the enterprise for the store and other storesassociated with the enterprise (retailer).

Online transaction system 120 comprises interfaces and correspondingsoftware by which customers perform online transactions with a retailer,such as via browsing items, storing selected items in a virtual cart,and checking out (paying for) items in the virtual cart. The onlinetransaction system 120 can be web-based and/or mobile app-based. Virtualcart data provided in real time from online transaction system 120 toenterprise data store 110 or provided via an Application ProgrammingInterface (API) in real time to price alert manager 190 during an onlineshopping session.

Transaction terminals 130 comprise peripheral devices (scanners,printers, media acceptors/dispensers, weigh scales, PersonalIdentification Number (PIN) pads, card readers, etc.) and correspondingsoftware for performing customer checkouts associated with transactions,Real-time item and transaction data provided by terminals to enterprisedata store 110.

User devices 140 comprise peripherals (touchscreens, cameras, etc.) andcorresponding software for performing customer transactions using a webbrowser or a mobile application (app). Real time transaction dataprovided by any app to enterprise data store 110.

EPR system 150 comprises devices and corresponding software and userinterfaces for enterprise transactions, inventory management, itempurchase orders, etc. Any item price in a given ERP operation is sent,via an API, to price alert manager 190 for price anomaly and priceconformance processing.

Item space mapper and similarity manager 170 initially generates vectorsfor item codes in a product catalogue (obtained from enterprise datastore 110) using transaction histories (again, obtained from enterprisedata store 110). In this manner, the item codes in the product catalogueare assigned vectors that map to multidimensional space. Each vectorlinked to the item codes of the product catalogue. “Item embedding” isapplied using a “Word2Vec” algorithm. Word2Vec is a group of algorithmsused primarily in the field of Natural Language Processing (NLP) formachine translation. The Word2Vec takes as its input a large corpus oftext (product catalogue of item codes and transaction histories fromenterprise data store 110) and produces a vector space of typicallyseveral hundred dimensions (multidimensional space), each unique word(item code) in the corpus being assigned a corresponding unique vectorplotted in the multidimensional space. In this way, item codes thatshare common contexts within the transaction histories are plotted inclose proximity to one another within the multidimensional space. Thetransaction histories are provided as sentences to the Word2Vecalgorithm and the words are the item codes (all words available areidentified from the product catalogue). Mathematical calculations can beapplied on the vectoral numeric representations (vectors) for the itemcodes.

Once the product catalogue and transaction histories are processed tocreate the item code vectors for the items, item space mapper andsimilarity manager 170 can be provided a given item code (as input), thegiven item code representing an item in a given transaction. Aconfigured number of N similar item codes can be identified based ondistances between the given item's mapped context within themultidimensional space and other mapped items' contexts plotted withinthe multidimensional space. The output produced by the Word2Vecalgorithm is similar item codes (replacement items) along withsimilarity scores (which correspond to the distances within themultidimensional space between the position of the provided item codeand the positions of similar item codes), Item space mapper andsimilarity manager 170 can determine the similar item codes to provideprice alert manager 190 based on a preset threshold (which can be set bythe retailer) value or range of values and/or based on a predefinednumber of top similarity scores.

Item space mapper and similarity manager 170 provides the similar itemcodes along with the similarity scores to price alert manager 190. Pricealert manager 190 obtains a catalogue price for the item code associatedwith a given transaction and the catalogue prices of each of the similaritem codes. A median price is calculated for the similar item codes. Theactual item price for the item of the transaction (including anytransaction item coupon/discount or item price override for thetransaction) is then compared to the median price for the prices of thesimilar item codes and a price deviation is derived. The price deviationcan be above or below the median price. The price deviation is thencompared to a retailer-set deviation threshold amount and when the pricedeviation exceeds or falls below the threshold amount, price alertmanager 190 sends a real-time alert to online transaction system 120,transaction terminal 130, user device 140, ERP system 150, and/ormanagement app 160 for further processing or handling during thetransaction.

As system 100 is deployed, feedback is monitored for the actualtransactions that were provided price alerts for item prices that felloutside the retailer-defined threshold amount. The feedback is anindication as to an action taken based on the alert, such as ignored(the anomalous item price was allowed for the transaction) or price wasadjusted for the transaction. A change in item price for a transactionis considered to be positive feedback whereas no change in the itemprice following a price alert is considered to be negative feedback.

One or more MLAs 180 are trained on input comprising item code vectorsplotted within multidimensional space, a current transaction priceassociated with the item, a current catalogue price for the item, anytransaction discount or transaction override item price for the item,and item prices for items similar to the item (based on a highestsimilarity score for the similar items returned by the item space mapperand similarity manager 170). The trained MLAs 180 represented amachine-learning model used for item price alerts based on itemaffinities and prices of items. Thus, as used herein MLA 180 may also bereferred to as a machine-learning model.

The trained result to which the MLAs 180 configure to achieve based onthe provided input parameters is a determination as to whether areal-time price alert for a given item of a current transaction is to beprovided or not provided. During training, anomalous prices andacceptable prices are identified as the results expected to be providedby the MLAs 180.

Once the MLAs 180 are trained a machine-learning model for price alertsis ready for use during transaction processing by price alert manager190, price alert manager 190 receives in real-time an item price for atransaction along with any item discount in price or other item priceoverride. Item space mapper and similarity manager 170 returns similaritem codes and corresponding similarity scores between the similar itemcodes and the item code associated with the transaction. Price alertmanager 190 obtains catalogue prices for the item of the transaction andfor each of the similar items. Price alert manager 190 provides theitem's catalogue price, actual transaction item price, any transactionitem price discount or override, and catalogue prices for the similaritems to MLA 180, and MLA 180 returns a decision as to whether a pricealert should be raised or not to price alert manager 190.

It is to be noted, that price alert manager 190 may determine on its ownwhether a price alert is needed without the assistance of MLA 180 bycalculating a median price for the similar items and comparing thedeviation in the actual item price (including any discount or item priceoverride) against the retailer-defined deviation threshold.

Furthermore, both MLA 170 and price alert manager 190 may separately andindependently determine whether a price alert is needed for a given itemprice of a transaction. This may be useful to achieve an accuracy ratewith the MLA 170 before removing the alert processing decision fromprice alert manager 190 and allowing MLA 170 to make price alertdecisions.

The machine-learning model associated with the MLAs 180 are continuallyretrained based on the feedback. This ensures that item price alerts aretailored to a given store's item pricing strategy and goals. In thisway, the accuracy of item price alerts performed by the machine-learningmodel is continuously improving and learning.

It is also to be noted that retailer's may remove a need to provide anddefine any item price deviation threshold amounts for items of thecatalogue once the MLAs 180 are fully deployed within the transactionprocessing workflow.

An example processing context associated with system 100 to determinewhether a price alert is to be sent or not may include pasta #1 having aprice of $8, pasta #2 having a price of $9, and pasta #3 having a priceof $10. Price alert manager 190 will not send any price alerts for anyof these prices during transactions because they do not standout fromone another. In a second context, pasta #1 has a price of $5, pasta #2has a price of $9, and pasta #3 has a price of $10; however, pasta #1has a promotion/discount; here, price alert manager 190 will not raise aprice alert because pasta #1 is associated with a known price discount.In a third context, pasta #1 has a price of $5, pasta #2 has a price of$9, and pasta #3 has a price of $10; there a no known promotions withany of the pastas; as a result, price alert manager 190 raises a pricealert for the transaction having pasta #1 with a price of $5 becausethis is abnormal price (because price alert manager 190 determined that$5 deviated from the price median of the pastas #1, #2, and #3 by morethan a retailer-defined threshold or because trained MLAs 180 determinedthe anomaly).

Components 170-190 may be provided as a web-based and/or cloud-basedservice to retailers wherein an API to the service is provided to accesseach retailer's enterprise data store 110 and communicatesubstitute/replacement item codes during transactions. The API permits amanagement app 160 for use by managers to obtain price alerts duringtransactions, such that managers can identify mistakes or fraud in realtime of employees checking customers out during transactions orcustomers performing self-checkouts.

User-operated devices 140 can be any consumer-operated device, such aswearable processing devices, voice-enabled network appliances(Internet-of-Things (IoTs) devices), laptops, desktops, tablets,network-based vehicle-integrated devices, and others. Devices 140 canalso be operated by employees of a retailer that utilize price alertmanager 150. Devices 140 utilize retailer-provided interfaces (web-basedand/or app-based interfaces) to perform shopping and transaction basketcheckouts with transaction services of network servers 120.

Transaction terminals 120 can be Point-Of-Sale (POS) terminals,Self-Service Terminals (SSTs), staff-operated mobile devices, and/orkiosks.

FIG. 1B is a diagram representing a process flow 100B of the system ofFIG. 1A, according to an example embodiment.

FIG. 1B illustrates a more fine-grain view of some components associatedwith system 100A.

Transaction data manager 111 provides transaction data from enterprisedata store to item space mapper and similarity manager 170. Item spacemapper and similarity manager 170 generates the multidimensional vectorspace and unique vectors plotted within that space for each item code ofthe item catalogue 112.

Price alert manager 190 trains the MLAs 180 based on item cataloguecodes, similar item codes and their catalogue item prices, realtransaction item prices along with any item discounts or item priceoverrides, loyalty transaction data 113, and feedback obtained acrossmultiple channels where transaction were conducted by online transactionsystem 120, ERP system 150, and management app 160.

Subsequently, when any given transaction of a customer for a transaction(via online transaction system 120 or ERP system 150), an item'stransaction price can be evaluated in real time by price alert manager190 and/or MLAs 180 to determine whether a real-time price alert for anitem prices of the transaction should or should not be raised to onlinetransaction system 120, ERP 150, and/or management app 160. Price alertmanager 190 provides as input to MLA 180, the item code, the itemcatalogue price, the item prices of similar items to the transactionitem (based on a configured number of highest similarity scores returnedby item space mapper and similarity manager 170, the actual transactionprice being processed for the item with the transaction, and anytransaction item price discount or transaction item price override. MLA180 provides as output a decision as to whether a price alert should beraised or not raised, Price alert manager 190 using an API tocommunicate a price alert for the corresponding item of thecorresponding transaction to the online transaction system 120, ERPsystem 150, and/or management app 160). Results positive or negative arefed back to price alert manager 190 through the API or derived by pricealert manager 190 from final transaction data or sales date in the caseof management app 160. The feedback is used in subsequent trainingsessions of MLA 180.

In an embodiment, components 110-113 and 170-190 are provided as asingle cloud-based service to components 120, 150, and 160 via an API.

These and other embodiments are now discussed with reference to theFIGS. 2-3 .

FIG. 2 is a diagram of a method 200 for real-time alerts for abnormalitem pricing, according to an example embodiment. The software module(s)that implements the method 200 is referred to as a “price alertmanager.” The price alert manager is implemented as executableinstructions programmed and residing within memory and/or anon-transitory computer-readable (processor-readable) storage medium andexecuted by one or more processors of a device. The processor(s) of thedevice that executes the price alert manager are specifically configuredand programmed to process the price alert manager. The price alertmanager has access to one or more network connections during itsprocessing. The network connections can be wired, wireless, or acombination of wired and wireless.

In an embodiment, the device that executes the price alert manager is aserver. In an embodiment, the server is a cloud processing environmentthat comprises multiple servers cooperating with one another as a singleserver. In an embodiment, the server is a Local Area Network (LAN)server.

In an embodiment, the price alert manager is all of or some combinationof 170-190.

In an embodiment, the price alert manager performs the processingdiscussed above with system 100A and process flow 100B.

In an embodiment, the price alert manager is provided as a SaaS to aplurality of enterprises, each enterprise having a subscription relevantto its customers and enterprise data store 110.

At 210, the price alert manager receives an item code and a transactionitem price for an item being processed in a transaction.

In an embodiment, at 211, the price alert manager receives a coupon, ora price override associated with the transaction item price in thetransaction.

At 220, the price alert manager obtains a group of other item codes thatare similar to the item code based on transaction contexts oftransaction histories having the item code and the other item codes.

In an embodiment of 211 and 220, at 221 the price alert managerdetermines the group of other item codes based on distances between anitem vector plotted in a multidimensional space for the item code andother item code vectors plotted in the multidimensional space for theother item codes.

In an embodiment of 221 and at 222, the price alert manager identifiesthe distances as being within a threshold distance.

At 230, the price alert manager acquires a catalogue item price for theitem code and other catalogue items' prices for the other item codes.

At 240, the price alert manager determines whether to send a real-timeprice alert for the item code based at least on the transaction itemprice, the catalogue item price, and the other catalogue items' prices.

In an embodiment of 222 and 240, at 241, the price alert managercalculates a median price from the catalogue item price and the othercatalogue items' prices.

In an embodiment of 241, at 242, the price alert manager calculates adeviation between the transaction item price and the median price andsends the real-time price alert when the deviation falls above of belowthe median price by a threshold amount.

In an embodiment of 241, at 243, the price alert manager calculates adeviation between the transaction item price and the median price anddetermines not to send the real-time price alert when the deviationadjusted for the coupon or the price override is within a thresholdamount of the median price.

In an embodiment, at 244, the price alert manager calculates a medianprice from the catalogue item price and the other catalogue items'prices. The price alert manager further calculates a deviation betweenthe transaction item price and the median price and sends the real-timeprice alert when the deviation falls above or below the median price bya threshold amount.

In an embodiment, at 245, the price alert manager obtains a decisionthat indicates whether to send the real-time price alert from a trainedmachine learning algorithm associated with a machine-learning model.

In an embodiment of 245 and at 246, the price alert manager provides asinput to the machine-learning model (before 245), the transaction itemprice, the catalogue item price, and the other catalogue items' prices,and transaction details for the transaction.

In an embodiment, at 247, the price alert manager determines to send thereal-time price alert and sends the real-time price alert to an ERPsystem that is associated with processing the transaction.

In an embodiment of 247 and at 248, the price alert manager sends thereal-time price alert to the ERP system via an API.

FIG. 3 is a diagram of another method 300 for real-time alerts forabnormal item pricing, according to an example embodiment. The softwaremodule(s) that implements the method 300 is referred to as an “itemprice anomaly manager.” The item price anomaly manager is implemented asexecutable instructions programmed and residing within memory and/or anon-transitory computer-readable (processor-readable) storage medium andexecuted by one or more processors of a device. The processors thatexecute the item price anomaly manager are specifically configured andprogrammed to process the item price anomaly manager. The item priceanomaly manager has access to one or more network connections during itsprocessing. The network connections can be wired, wireless, or acombination of wired and wireless.

In an embodiment, the device that executes the item price anomalymanager is a server. In an embodiment, the server is a cloud processingenvironment that comprises multiple servers cooperating with one anotheras a single server. In an embodiment, the server is a LAN server that islocal to a retail store.

In an embodiment, the item price anomaly manager is all or somecombination of 170-190, process flow 100B, and/or the method 200.

The item price anomaly manager presents another and, in some ways,enhanced processing perspective to that which was described above withthe FIG. 2 .

At 310, the item price anomaly manager generates item vectors for itemcodes of a product catalogue.

In an embodiment, at 311, the item price anomaly manager generates eachitem vector based on transaction contexts for the corresponding itemcode.

At 320, the item price anomaly manager plots each item vector inmultidimensional space.

At 330, the item price anomaly manager receives a transaction item codefor a transaction item and a transaction item price for the transactionitem during a transaction.

At 340, the item price anomaly manager identifies similar item codes tothe transaction item based on the item vectors plotted in themultidimensional space.

In an embodiment of 311 and 340, at 341, the item price anomaly manageridentifies each similar item code as being associated with acorresponding item vector that is within a threshold distance of atransaction item vector for the transaction item within themultidimensional space.

At 350, the item price anomaly manager obtains a first item price forthe transaction item code and second items' prices for the similar itemcodes from the product catalogue.

At 360, the item price anomaly manager makes a decision as to whether tosend a real-time price alert to a resource associated with processingthe transaction based at least on the transaction item price, the firstitem price, and the second items' prices.

In an embodiment, at 361, the item price anomaly manager obtains thedecision from a trained machine-learning algorithm based on amachine-learning model.

At 370, the item price anomaly manager sends the real-time price alertvia an API to the resource when indicated by the decision.

In an embodiment of 361 and 370, at 371, the item price anomaly managerobtains a feedback (result) from the resource or from transaction dataassociated with a completed transaction when the real-time price alertwas sent to the resource but the transaction item price was not changedfor the transaction item. The item price anomaly manager uses thefeedback to retain the machine-learning algorithm or model withtransaction details for the transaction and the feedback.

In an embodiment, at 372, the item price anomaly manager calculates amedian price from the first item price and the second items' prices anddetermines a deviation between the transaction item price and the medianprice. The item price anomaly manager makes a decision to send thereal-time price alert when the deviation falls above or below the medianprice by a threshold amount.

It should be appreciated that where software is described in aparticular form (such as a component or module) this is merely to aidunderstanding and is not intended to limit how software that implementsthose functions may be architected or structured. For example, modulesare illustrated as separate modules, but may be implemented ashomogenous code, as individual components, some, but not all of thesemodules may be combined, or the functions may be implemented in softwarestructured in any other convenient manner.

Furthermore, although the software modules are illustrated as executingon one piece of hardware, the software may be distributed over multipleprocessors or in any other convenient manner.

The above description is illustrative, and not restrictive. Many otherembodiments will be apparent to those of skill in the art upon reviewingthe above description. The scope of embodiments should therefore bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

In the foregoing description of the embodiments, various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting that the claimed embodiments have more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus, the following claims are herebyincorporated into the Description of the Embodiments, with each claimstanding on its own as a separate exemplary embodiment.

The invention claimed is:
 1. A method for raising an alert to a systemfor a specific item based on a price analysis and deviations in a givenprice for the specific item, comprising: providing instructions to aprocessor of a server causing the processor to perform operations,comprising: generating item vectors for item codes of a productcatalogue by and for each item code, generating coordinates within amultidimensional space associated the corresponding item code andprevious transaction item codes purchased with the corresponding itemcode in previous transactions associated with the corresponding itemcode to generate the corresponding item vector for the correspondingitem code; plotting each item vector represented by the correspondingcoordinates in the multidimensional space as an item context for thecorresponding item code within the multidimensional space; receiving atransaction item code for a transaction item and a transaction itemprice for the transaction item during a transaction, wherein thetransaction item price is any adjusted price for the transaction itembased on any discount applied to the transaction item during thetransaction; generating a current item vector based on the transactionitem code and other item codes present in the transaction; plotting thecurrent item code in the multidimensional space; identifying similaritem codes to the transaction item based on the item vectors plotted inthe multidimensional space by comparing the item vectors associated withthe transaction item against the item vectors associated with thesimilar item codes within the multidimensional space; obtaining a firstitem price for the transaction item code and second items' prices forthe similar item codes from the product catalogue; and making a decisionas to whether to send a real-time price alert to a resource associatedwith processing the transaction based at least on the transaction itemprice, the first item price, and the second items' prices; sending thereal-time price alert via an Application Programming Interface (API) tothe resource when indicated by the decision causing the resource toperform transaction processing for the transaction based on thereal-time price alert received by the resource during the transaction;and obtaining a feedback from the resource when the real-time pricealert was sent but the transaction item price was not changed for thetransaction item of the transaction and training a machine-learningalgorithm with transaction details for the transaction, the alert, andthe feedback to predict future decisions, wherein the feedback is anindication as to an action taken based on the alert, wherein the actiontaken can include ignored with no price change to the transaction itemprice or a price change was made to the transaction item price based onthe alert.
 2. The method of claim 1, wherein generating the item vectorsfurther includes generating each item vector based on transactioncontexts for the corresponding item code appearing in the correspondingprevious transactions.
 3. The method of claim 2, wherein identifyingfurther includes identifying each similar item code as being associatedwith a corresponding item vector that is within a threshold distance ofa transaction item vector for the transaction item within themultidimensional space.
 4. The method of claim 1, wherein making furtherincludes obtaining the decision from the machine-learning algorithm. 5.The method of claim 1, wherein making further includes, calculating amedian price from the first item price and the second items' prices,determining a deviation between the transaction item price and themedian price, and making the decision to send the real-time price alertwhen the deviation falls above or below the median price by a thresholdamount.
 6. A system for raising an alert to a system for a specific itembased on a price analysis and deviations in a given price for thespecific item, comprising: at least one processing device having atleast one processor configured to execute instructions from anon-transitory computer-readable storage medium; the instructions whenexecuted by the at least one processor from the non-transitorycomputer-readable storage medium cause the at least processor to performoperations comprising: mapping item codes from a product catalogue tovectors plotted in multidimensional space by and for each item code,generating coordinates within the multidimensional space associated thecorresponding item code and previous transaction item codes purchasedwith the corresponding item code in previous transactions associatedwith the corresponding item code to generate the corresponding vectorfor the corresponding item code and plotting each vector using thecorresponding coordinates as an item context for the corresponding itemcode within the multidimensional space; determining similarities betweena transaction item code associated with a transaction item of atransaction being processed and other item codes based on thecorresponding vectors plotted in the multidimensional space bygenerating a current item vector based on the transaction item code andthe other item codes present in the transaction, plotting the currentitem vector in the multidimensional space and identifying thesimilarities by evaluating the current item vector against the vectorsplotted in the multidimensional space; obtaining a transaction itemprice being used from the transaction for the transaction item;obtaining a first item price for the transaction item code from acatalogue wherein the first item price is any adjusted price for thetransaction item based on any discount applied to the transaction itemduring the transaction; obtaining second items' prices for the otheritem codes; calculating a median price from the first item price and thesecond items' prices; calculating a deviation between the transactionitem price and the median price; and sending a real-time price alert toa resource associated with processing the transaction when the deviationfalls above or below the median price by a threshold amount causing theresource to perform transaction processing for the transaction based onthe real-time price alert received by the resource during thetransaction; and obtaining a feedback from the resource when thereal-time price alert was sent but the transaction item price was notchanged for the transaction item of the transaction and training amachine-learning algorithm with transaction details for the transaction,the alert, and the feedback to predict future decisions, wherein thefeedback is an indication as to an action taken based on the alert,wherein the action taken can include ignored with no price change to thetransaction item price or a price change was made to the transactionitem price based on the alert.
 7. The system of claim 6, wherein theresource is an online transaction system, a transaction terminal, anEnterprise Resource Planning (ERP) system, or a management application.